Review Article

Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments

Table 5

Energy/power control method.

Energy/power control methodWorksDescription

(1) Selection of devices/scheduling[73]Selection of devices in a cluster or collection of clusters such that maximum power consumption limit is followed + data partitioning and scheduling of computations
[35]Selection of cores for a configuration minimizing energy consumption
[75]Using GPUs for optimization/generation of parity data
[39]Selection of best GPU architectures in terms of performance/energy usage point of view
[58]Specific scheduling and switching off unused cluster nodes
[33, 71]Task partitioning and scheduling
[44]Task scheduling, a two-stage energy-efficient temperature-aware task scheduling algorithm is proposed: in the first system, dynamic energy consumption under task deadlines, in the second temperature profiles of processors, are improved
[76]Application assignment to virtual and physical nodes of the cloud
[66]Workload placement in a data center
[68]Proposal of RMAP—a resource manager that minimizes average turnaround time for jobs provides an adaptive policy that supports overprovisioning and power-aware backfilling

(2) DVFS/DFS/DCT[49]For MPI applications with the goal not to impact performance
[47]Uniform frequency power-limiting investigates results for the fixed frequency mode, minimum power level assigned to a job, and automatic mode with consideration of available power
[45]Core and uncore frequency scaling of CPUs
[55, 56]Minimization of energy usage through DVFS on particular nodes
[52]DFS, DCT
[14]DVFS, DCT
[37]Control of frequency on a GPU
[41]DVFS with dynamic detection of computation phases (memory and CPU bound)
[59]DVFS with a posteriori (using logs) detection and prioritization of computation phases (memory and CPU bound)
[61]Sysfs interface is used
[63, 64]DCT, combined DVFS/DCT
[65]Sysfs interface
[72]Setting the frequency according to the established computing center policies

(3) Power capping[24]Using Intel RAPL for power management
[40]Using Intel RAPL for analyzing energy/performance trade-offs with power capping for parallel applications on modern multi- and manycore processors
[42]Using PAPI and Intel RAPL
[62]Using Intel RAPL
[46]Using Intel’s power governor tool and Intel RAPL

(4) Application optimizations[54]Theoretical consideration of optimizations of an application that results in improvement of performance countervalues
[36]Finding an optimal GPU configuration (in terms of the number of threads per block and the number of blocks)
[53, 57]Control of CPU frequency, spinning down the disk, and network speed scaling
[43]Exploration of various loop scheduling ways, chunk sizes, optimization levels, and thread counts

(5) Hybrid[30]Software + RAPL, the proposed PUPiL approach combines hardware’s fast reaction time with flexibility of a software approach
[48]Scheduling/software + resource management (including RAPL), the proposed algorithm takes into account real power and energy consumption
[34]Concurrent kernel execution + DVFS
[50, 74]Scheduling + DVFS
[38]Scheduling + DVS for minimization of temperature and meeting task deadlines
[51]Scheduling jobs and management of resources and DVFS
[77]Selection of the resources for a given user request, with VM migration and putting unused machines in the sleep mode
[60]Workload distribution + DVFS-based multiobjective optimization
[69]Polling, interrupt-driven execution (relinquishing CPU and waiting on a network event), DVFS power levers
[70]Selection of nodes in an overprovisioned HPC clusters and Intel RAPL